title: Absence seizure epilepsy detection using linear and nonlinear EEG analysis methods.
abstract: In this study, we investigated three measures capable of detecting absence seizures with increased sensitivity based on different underlying assumptions. Namely, an information-based method known as Approximate Entropy, a nonlinear alternative (Order Index), and a linear variance analysis approach. The results on the long-term EEG data suggest increased accuracy in absence seizure detection achieving sensitivity as high as 97.33% with no further application of any sophisticated classification scheme.
authors.forename: Vangelis
authors.initials: V
authors.lastname: Sakkalis
authors.forename: Giorgos
authors.initials: G
authors.lastname: Giannakakis
authors.forename: Christina
authors.initials: C
authors.lastname: Farmaki
authors.forename: Abdou
authors.initials: A
authors.lastname: Mousas
authors.forename: Matthew
authors.initials: M
authors.lastname: Pediaditis
authors.forename: Pelagia
authors.initials: P
authors.lastname: Vorgia
authors.forename: Manolis
authors.initials: M
authors.lastname: Tsiknakis
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D004569:Electroencephalography; D019277:Entropy; D004832:Epilepsy, Absence / Q000175:diagnosis*; D006801:Humans